During the current grant period, we have developed two models of the impact of missense SNPs on protein function in vivo. Through this work, and that of others, we now have a clearer understanding of the molecular mechanisms that lead to monogenic disease. We have also developed a web resource which integrates our results with a large range of other information relevant to disease. Goals for next phase fall into three categories: (1) Exploiting the present results to address basic questions concerning the relationship between genetic variation and disease. Specifically: What is the distribution of mechanisms by which mis-sense SNPs influence protein function;how common are epistatic (non-linear) interactions between SNPs with the same protein molecule and across protein-protein interfaces;what are the characteristics of the approximately 1500 proteins involved in monogenic disease, versus all the others;which SNPs are most significant in directly and indirectly affecting particular biological processes and susceptibility to common diseases? (2) Improving and extending SNP analysis methods, both to obtain a more extensive and reliable set of deleterious mis-sense SNPs, and to include analysis of SNPs that influence disease susceptibility through other processes. New models will be developed for effects on transcription, message processing and translation. Available database information will be augmented by a combination of literature analysis and soliciting input from appropriate members of the scientific community. (3) Maintain and enhance the web resource to increase its utility to the research community. Functionality will be extended to allow users to input their own SNPs and receive a real time analysis from both models;a user annotation interface will be developed, to capture knowledge on the role of amino acids altered mis-sense SNPs, as well as which genes are most relevant to disease, and what buffering mechanisms shield the phenotype from particular deleterious SNPs;additional information sources will be incorporated, including SNP analysis by others. Relevance to public health: Many common human diseases, such as heart attack, Alzheimer, asthma and diabetes, are partly inherited, through specific DNA features. At present, there is no common disease for which these inheritance mechanisms are understood. This work will provide specific insight into a number of diseases, and improved understanding of the inheritance process.